Farzad Pourboghrat

A Simplified Dynamic Model of Grid-Connected Fuel-Cell Generators

This article describes a reduced-order dynamic model for a grid-connected fuel-cell power plant that is suitable for preliminary stability assessment. Generic voltage and power control loops are included. The model is applied to a distributed utility that uses fuel cells and gas turbines to investigate the nature and magnitude of their interaction. The studies presented in the paper show the effect of the mix between fuel cell and gas turbine generation on the system stability. The developed model, being simple, could provide a useful tool for the planning of distributed generation.

Adaptive control of dynamic mobile robots with nonholonomic constraints

Abstract This paper presents adaptive control rules, at the dynamics level, for the nonholonomic mobile robots with unknown dynamic parameters. Adaptive controls are derived for mobile robots, using backstepping technique, for tracking of a reference trajectory and stabilization to a fixed posture. For the tracking problem, the controller guarantees the asymptotic convergence of the tracking error to zero. For stabilization, the problem is converted to an equivalent tracking problem, using a time varying error feedback, before the tracking control is applied. The designed controller ensures the asymptotic zeroing of the stabilization error. The proposed control laws include a velocity/acceleration limiter that prevents the robot’s wheels from slipping.

Exponential stabilization of nonholonomic mobile robots

Abstract The problem of point-to-point control design for differentially steered nonholonomic mobile robots is considered in this paper. The control variables are derived using Lyapunov’s stability technique and are piecewise continuous. The proposed control law guarantees the exponential stability of the closed-loop system and ensures the convergence of the position and the orientation of the robot to their desired fixed values.

Local sliding control for damping interarea power oscillations

In this paper, a sliding control (SC) algorithm design is considered for damping local power oscillations in a multiple area power transmission system. The control algorithm utilizes static Var compensators (SVC) to supply reactive power to the transmission system to stabilize the system in the event of faults. The controller is capable of achieving full utilization of the SVC and is insensitive to parameter variations and modeling errors. In general, more than one SVC is needed to effectively damp modes of power oscillation in a multiple area system. The simulation results for multiple area power system show the effectiveness of the proposed sliding controller in damping the interarea power oscillations, and in enhancing the stability as well as loadability of the transmission system.

Power conditioner control and protection for distributed generators and storage

This paper describes an integrated control and protection scheme for the power conditioner used by the medium rating grid-connected distributed generators and storage devices. The proposed control scheme consists of two loops: a steady-state loop that achieves optimum harmonic output by selective elimination of low-order harmonics and a transient loop based on space vector methods that enhances the transient response of the generator and provides overcurrent protection and fault rejection. The paper presents simulation studies of a grid-connected storage device equipped with a controller based on the proposed scheme. The results of these studies demonstrate the robustness of the controller to varying line conditions and disturbances.

Dynamic Neural Networks for Modeling and Control of Nonlinear Systems

Abstract This paper considers the design of a dynamic neural network (DNN) for modeling of a class of nonlinear systems for the purpose of real-time control. The primary contribution of the paper is in developing a DNN estimator with a stable training technique for on-line modeling of unknown (black box) dynamic nonlinear systems. The DNN acts as a generic model of the system, which can be trained on-line and, hence, can be utilized for the implementation of an adaptive model-based control strategy. The training of the network is based on a novel scheme that arranges the outputs of the hidden layer of the DNN into a set of basis functions. This allows for the derivation of a stable rule for the training of the DNNs weights and does not require random initialization of the weights.

Optimal control of nonlinear systems using RBF neural network and adaptive extended Kalman filter

This paper presents a nonlinear optimal control technique based on approximating the solution to the Hamilton-Jacobi-Bellman (HJB) equation. The HJB solution (value function) is approximated as the output of a radial basis function neural network (RBFNN) with unknown parameters (weights, centers, and widths) whose inputs are the systems states. The problem of solving the HJB equation is therefore converted to estimating the parameters of the RBFNN. The RBFNNs parameters estimation is then recognized as an associated state estimation problem. An adaptive extended Kalman filter (AEKF) algorithm is developed for estimating the associated states (parameters) of the RBFNN. Numerical examples illustrate the merits of the proposed approach.

Adaptive SDRE-Based Nonlinear Sensorless Speed Control for PMSM Drives

This paper presents a new adaptive nonlinear control approach for permanent magnet synchronous motor (PMSM) drives, without requiring speed sensors. The proposed approach is based on state-dependent Riccati equation (SDRE) control technique and its real-time computation method by gradient-type neural networks, that allows one to control PMSM online. For this purpose, the unknown parameters of the PMSM drive; stator resistance, load torque, and motor speed, are also estimated using an extended Kalman filter (EKF) algorithm. The resulted adaptive algorithm is independent from any initial conditions, relatively fast to achieve nonlinear system speed tracking control, numerically not complex, and easily applicable to real-time control of PMSM drives. This new adaptive approach for sensorless control of PMSM drives is demonstrated through an illustrative simulation for the proof of concept.

Online solution of state dependent Riccati equation for nonlinear system stabilization

A number of computational methods have been proposed in the literature for synthesizing nonlinear control based on state-dependent Riccati equation (SDRE). Most of these methods are numerically complex or depend on correct initial conditions. This paper presents a new and computationally efficient online method for the design of stabilizing control for a class of nonlinear systems based on state-dependent Riccati equation using a gradient-type neural network. Moreover, the proposed network is proven to be stable. The efficacy of this approach is demonstrated through illustrative examples for the proof of concept.

Active noise blocking: Non-minimal modeling, robust control, and implementation

This paper presents a new modeling and robust control approach for active noise blocking (ANB). The proposed modeling technique is based on a new non-minimal state-space realization (NSSR) of continuous-time multiple-input multiple-output (MIMO) linear time-invariant (LTI) systems. The NSSR model generates a non-minimal set of states for a given system, using measured inputs and outputs, without differentation. From the NSSR model, using an H∞ model reduction technique, a reduced-order state space (RSS) model is derived with known states. A multi-model H∞ state-feedback (MHSF) control is then designed, in an LMI framework, for multiple RSS models of the system. This control design has an increased robustness against modeling uncertainty when different frequency responses of the system belong to a bounded convex set. Hardware experiments using a digital signal processor (DSP) have been carried out in order to verify the applicability and the performance of the proposed NSSR-based modeling and vibration control of a plate for active noise blocking (ANB) in a 3D acoustic enclosure.